Classical approaches [1]}, [2]}, [3]} resorted to a hardcoded quantitative threshold to define the proximal distance for neighborhoods. The use of deep learning improves the neighborhood concept by learning a custom threshold for each pedestrian [4]}.
MX-LSTM [5]} determines neighborhoods around pedestrians visually based on the fixed visual horizon of pedestrian. Within this imaginary area, pedestrians spatial states get pooled. To this end, MX-LSTM only considers the correlation between head pose and speed. In the aforementioned works, generalization can be problematic as the models rely on fixed assumptions that pertain to specific scenes. Recent works introduced additional features such as the looking angle of pedestrians [5]}. These approaches and GAN-based approaches resorted to dedicating several pooling layers for treating multiple features [7]}, [8]} or multiple trajectories states [9]}, [10]}, [11]} which will require additional transformation and pooling layers or assigning separate LSTM to each pedestrian in order to treat all the features together.
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